Computer vision systems and methods are frequently used to automate assembly tasks, such as performed by industrial robots. However, small, shiny or specular objects, which are common in many industrial environments, still present a great challenge for computer vision applications. Objects with mirror-like, transparent or translucent surfaces possess material properties that are frequently treated as noise sources, and conventional techniques attempt to suppress the noise. This means that objects which are either highly specular or have significant translucency cannot be handled by conventional computer vision techniques because it is difficult to suppress these material.
Computer vision has been used for bin picking, where the main problem is to determine a pose of an object and in a bin of identical objects. As defined herein, the 6D pose is the 3D position and 3D orientation of the object. Development of computer vision systems is a challenge because of specular reflections of metallic surfaces of industrial parts, and occlusions in a cluttered bin of many identical objects.
Model-based pose estimation determines 3D model to 2D image correspondences. Unfortunately, 3D-2D point correspondences are hard to obtain for industrial parts with textureless surfaces. The situation is particularly severe when multiple identical objects overlap each other in a bin and occlusions can occur.
Object contours provide rich information about object shapes and poses. Various contour matching methods are known. However, for specular objects, the contour information is difficult to obtain in a cluttered bin because the objects do not have appearance of their own in images, but rather the objects reflect the surrounding environment.
Range sensors are widely used for the pose estimation problem. Range data can be used to generate and verify object positions and shape of flexible industrial parts, such as cables. However in the presence of specularities, range sensors fail to produce accurate depth maps, and are comparably more expensive than camera based solutions.
Active illumination patterns can greatly assist computer vision methods. For example, the brightness of patches observed with varying illumination condition can be used to the estimate orientation of surface patches, and then matches them with the 3D model.
As stated above, specularities have generally been treated as sources of noise in machine vision methods. Most methods identify specularities and remove them as noise.
One image invariant method for highly specular and mirror-like surfaces exploits the fact that the surface normals do not change at regions of zero-curvature. For most industrial objects, this is not a very practical feature because the objects are typically made up of several planar surfaces and some corners, and the distinctive information about these objects is present at these corners and junctions.